For Successful Data Governance Avoid These Mistakes

You must have heard of the term data governance particularly with the entire buzz it has generated in recent times. Well, if you think data governance is not the modern rubric of anything to do with data then think again! Just try doing a search around and you are bound to get references in the realm of data warehousing, data ownership, data security, data quality etc. However, what exactly is data governance?

You must have heard of the term data governance particularly with the entire buzz it has generated in recent times. Well, if you think data governance is not the modern rubric of anything to do with data then think again! Just try doing a search around and you are bound to get references in the realm of data warehousing, data ownership, data security, data quality etc. However, what exactly is data governance?

In simple terms, data governance entails management of the availability, usability and security of the data in an enterprise. Did you know that your company may be ‘blessed’ with lots of accumulated data that could be leveraged for vital business insights? If you are in the dark about such prospects, then you are surely not alone as many other businesses around do not seem to have the right framework to tap into such invaluable data.

In other words, a data governance program is an important step that will help your company to treat data as an important asset by putting up rules, policies and procedures for data governance in place. Many companies have tried to implement enterprise data governance but few have succeeded. What is the secret behind all these failures? It is all down to pitfalls and most importantly worst practices that should serve as examples to any company looking to dive into data governance. In the following sections of this article, we look at the mistakes that you must steer away from when undertaking any data governance implementation. Take some notes!

Failure to have a data governance framework

Every successful data governance implementation has to have principles, decision rights and a governing body that will see to it that data is properly administered. Without this kind of framework, employees in various departments will incline to make decisions they desire keeping in mind that they will not be answerable to any mishap that might happen. That is just an example but it puts a lot of weight on the necessity of having proper control or someone to hold responsible for certain facades of data issues.

The outcome of such a scenario is lots of money and other resources being pumped on the IT department in a bid to figure out where the root of a data problem is. Keep in mind that there is no guarantee that solutions will be determined after such huge investments.

Emphasis solely on your IT Team

The goal of your IT department is to ensure the security of the data by protecting it from breach or loss. They do not really pay attention to what the data entails-after all they do not own the data. This means that if you use the IT department solely to implement data governance then your company might end up being disappointed.

It is the task of the senior level management and every stakeholder in the enterprise has to govern the data, implement the policies and the procedures that are set in relation to data governance.

Viewing data governance as a project

How many times do we see companies rush to restore data in the event that there is a data breach? Of course, such restoration efforts will come in the form of a data project that will be launched to ensure that there will be no repeat of such a breach. That is a wrong move!

In fact, data should be governed at all times, as long as there is new data flowing in and out of the company or if systems are being upgraded.

Poor Definition of Data Governance

Data governance and data management are two different terms that can be easily confused. If you are to implement proper data governance, then you should first properly differentiate the two sides. Data governance is the policy-making framework for enterprise data while data management involves execution of those data policies set by the company.

Data governance is more of a business driven process that will need to be placed in the hands of those in charge while data management is more of the IT department function that should be the responsibility of those protecting the data. Therefore, your company should clearly identify the difference between the two concepts and set the necessary policies for each.

Don’t Just Look to Satisfy a Regulator

When pressure is on a company to implement data governance for the sake of satisfying regulator, then such a company may tend to deliver the minimum requirements as quickly as possible. By focusing on satisfaction of regulator, companies will have burden of work. The other mistake is that using only checklist shows tasks to be completed and the consequences of failing to do so. People in organization will only do tasks that they expected earlier and ignore benefits. As a result, not all factors and facets of the data will be checked.

This is not the advisable route to take since a lot of work will have to be done over a short period unlike if you had data governance implemented already. What happens if a regulator changes or updates the checklist at frequent interval? Simple answer! You will be back to square one.

To avoid the disorder, the implementation of data governance should be considered as an ongoing process and involvement of the whole organization. By following this approach, organization should align data governance with comprehensive strategic objects. As a result, businesses will quickly enjoy advantages that were given to simply satisfying the regulators.

One important rule in data governance is to take your time to make sure you do everything right. It might call for patience and consume a lot of time but a phase-based approach will do the magic for your business!